HelpingAI2 9B i1 needs ~8.9 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~71 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
70.7 tok/s
TTFT
2739 ms
Safe context
62K
Memory
8.9 GB / 12.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 70.7 tok/s | 1494 ms | 62K |
| Coding | B | Runs well | 70.7 tok/s | 2739 ms | 62K |
| Agentic Coding | C | Tight fit | 70.7 tok/s | 3984 ms | 62K |
| Reasoning | B | Runs well | 70.7 tok/s | 3237 ms | 62K |
| RAG | C | Tight fit | 70.7 tok/s | 4980 ms | 62K |
How HelpingAI2 9B i1 (9B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C49 |
Q3_K_S | 3 | 4.4 GB | Low | C51 |
NVFP4 | 4 | 5.0 GB | Medium | C51 |
Q4_K_M | 4 | 5.5 GB | Medium | C52 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C51 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run HelpingAI2 9B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-9b-i1-gguf && lms server startYes, RTX 4070 Super 12GB can run HelpingAI2 9B i1 with a B grade (Runs well). Expected decode speed: 70.7 tok/s.
HelpingAI2 9B i1 (9B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 9B i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 4070 Super 12GB, HelpingAI2 9B i1 achieves approximately 70.7 tokens per second decode speed with a time-to-first-token of 2739ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B i1 on RTX 4070 Super 12GB receives a B grade with 70.7 tok/s and 62K context.
On RTX 4070 Super 12GB, HelpingAI2 9B i1 can safely use up to 62K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-9b-i1-gguf-on-rtx-4070-super-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: